AI Classifies Multi-Retinal Diseases

• Conditions: Deep Learning; Retinal Diseases
• Interventions: Device: Retinal multi-diseases diagnosed by DL algorithm; Other: Retinal multi-diseases diagnosed by expert panel

• Registration Number: NCT04592068
• Lead Sponsor: Beijing Tongren Hospital

Brief Summary

The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. The effectiveness and accuracy of the established algorithm will be evaluated in community derived dataset.

Detailed Description

Retinal diseases seriously threaten vision and quality of life, but they often develop insidiously. To date, deep learning (DL) algorithms have shown high prospects in biomedical science, particularly in the diagnosis of ocular diseases, such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and papilledema. However, there is still a lack of a single algorithm that can classify multi-diseases from fundus photography.

This cross-sectional study will establish a DL algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the evaluation indexes, such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

Study Timeline

• Status Verified: 2020-10
• Study First Submitted: 2020-10-13
• Study First Submitted QC: 2020-10-15
• Study First Posted: 2020-10-19
• Study Start: 2020-11-01
• Last Update Submitted: 2020-12-09
• Last Update Posted: 2020-12-11
• Primary Completion: 2021-11-01
• Study Completion: 2021-12-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 10000

Inclusion Criteria

• fundus photography around 45° field which covers optic disc and macula
• complete patient identification information;

Exclusion Criteria

• incomplete patient identification information

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Retinal multi-diseases diagnosed by DL algorithm	Retinal multi-diseases diagnosed by DL algorithm	-
Retinal multi-diseases diagnosed by expert panel	Retinal multi-diseases diagnosed by expert panel	-

Primary Outcome Measures

Name	Time	Method
Sensitivity and specificity	1 week	Taken the results of the expert panel as the gold standard, we will use sensitivity and specificity to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
Accuracy	1 week	Taken the results of the expert panel as the gold standard, we will use accuracy to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
Positive and negative predictive value	1 week	Taken the results of the expert panel as the gold standard, we will use positive and negative predictive value to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
Area under curve	1 week	We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the area under curve to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

Trial Locations

Locations (1)

Wen-Bin Wei; 🇨🇳 Beijing, Beijing, China